Data compression is a useful tool for reducing the bandwidth required for communication of data on a transmission channel.
Some current compression schemes, such as Huffman compression, require knowledge about the frequency of expected data words, while others rely on compression of repeated data words, such as run length encoding, or require state information. Both lossless and lossy codes are available. Huffman compression and run length encoding are examples of lossless compression, and MP3 is an example of lossy compression.
Codes requiring knowledge of the frequency of expected data, such as Huffman codes, are useful for applications like the English language, where knowledge of the frequency of letter occurrence in English words can be used. Also, frequency information can be derived from the data to select the data encoding scheme. However, these codes may not work well for data with a more uniform distribution. Run length encoding is good if there are long periods of repeated data, such as zeros, but is of little use when noise causes the data to vary.
MP3 uses characteristics of the human ear to eliminate inaudible information, and thus is a lossy compression scheme. It uses Huffman encoding as a coding method, which in addition to serving as an encoding mechanism, allows additional compression in areas of relatively uniform sounds. If the application does not relate to the human ear, then MP3 is not applicable. Even for audio applications such as Voice Over IP (VoIP), the data is already severely limited at the high end of the frequency range, so the human ear effects are already addressed to some degree. Also, stereophonic effects are not applicable because VoIP is monaural.
Therefore, there is a need in the art for improved apparatuses and methods for compressing data in communication networks. In particular, there is a need for data compression with reduced dependence on factors such as, state information, knowledge of the data characteristics, characteristics of human users, and stereophonic effects.